Journal of the Royal Statistical Society: Series B

最新文章

• High dimensional thresholded regression and shrinkage effect AI翻译
  作者：Zheng, Zemin; Fan, Yingying; Lv, Jinchi
  作者单位：University of Southern California
  摘要：High dimensional sparse modelling via regularization provides a powerful tool for analysing large-scale data sets and obtaining meaningful interpretable models. The use of non-convex penalty functions shows advantage in selecting important features in high dimensions, but the global optimality of such methods still demands more understanding. We consider sparse regression with a hard thresholding penalty, which we show to give rise to thresholded regression. This approach is motivated by its c...
• Selective inference on multiple families of hypotheses AI翻译
  作者：Benjamini, Yoav; Bogomolov, Marina
  作者单位：Tel Aviv University; Technion Israel Institute of Technology
  摘要：In many complex multiple-testing problems the hypotheses are divided into families. Given the data, families with evidence for true discoveries are selected, and hypotheses within them are tested. Neither controlling the error rate in each family separately nor controlling the error rate over all hypotheses together can assure some level of confidence about the filtration of errors within the selected families. We formulate this concern about selective inference in its generality, for a very w...
• Report of the Editors-2013 AI翻译
  作者：Roberts, G. O.; Van Keilegom, I.
• Improved estimation in cumulative link models AI翻译
  作者：Kosmidis, Ioannis
  作者单位：University of London; University College London
  摘要：For the estimation of cumulative link models for ordinal data, the bias reducing adjusted score equations of Firth in 1993 are obtained, whose solution ensures an estimator with smaller asymptotic bias than the maximum likelihood estimator. Their form suggests a parameter-dependent adjustment of the multinomial counts, which in turn suggests the solution of the adjusted score equations through iterated maximum likelihood fits on adjusted counts, greatly facilitating implementation. Like the ma...
• Split sample methods for constructing confidence intervals for binomial and Poisson parameters AI翻译
  作者：Decrouez, Geoffrey; Hall, Peter
  作者单位：University of Melbourne; University of California System; University of California Davis
  摘要：We introduce a new method for improving the coverage accuracy of confidence intervals for means of lattice distributions. The technique can be applied very generally to enhance existing approaches, although we consider it in greatest detail in the context of estimating a binomial proportion or a Poisson mean, where it is particularly effective. The method is motivated by a simple theoretical result, which shows that, by splitting the original sample of size n into two parts, of sizes n(1) and ...
• Preadjusted non-parametric estimation of a conditional distribution function AI翻译
  作者：Veraverbeke, Noel; Gijbels, Irene; Omelka, Marek
  作者单位：Hasselt University; North West University - South Africa; KU Leuven; Charles University Prague
• Association pattern discovery via theme dictionary models AI翻译
  作者：Deng, Ke; Geng, Zhi; Liu, Jun S.
  作者单位：Harvard University; Tsinghua University; Peking University
• On estimation efficiency of the central mean subspace AI翻译
  作者：Ma, Yanyuan; Zhu, Liping
  作者单位：Texas A&M University System; Texas A&M University College Station; Shanghai University of Finance & Economics
  摘要：We investigate the estimation efficiency of the central mean subspace in the framework of sufficient dimension reduction. We derive the semiparametric efficient score and study its practical applicability. Despite the difficulty caused by the potential high dimension issue in the variance component, we show that locally efficient estimators can be constructed in practice. We conduct simulation studies and a real data analysis to demonstrate the finite sample performance and gain in efficiency ...
• Multiple-change-point detection for auto-regressive conditional heteroscedastic processes AI翻译
  作者：Fryzlewicz, P.; Rao, S. Subba
  作者单位：University of London; London School Economics & Political Science; Texas A&M University System; Texas A&M University College Station
  摘要：The emergence of the recent financial crisis, during which markets frequently underwent changes in their statistical structure over a short period of time, illustrates the importance of non-stationary modelling in financial time series. Motivated by this observation, we propose a fast, well performing and theoretically tractable method for detecting multiple change points in the structure of an auto-regressive conditional heteroscedastic model for financial returns with piecewise constant para...
• Quick and easy one-step parameter estimation in differential equations AI翻译
  作者：Hall, Peter; Ma, Yanyuan
  作者单位：University of Melbourne; University of California System; University of California Davis; Texas A&M University System; Texas A&M University College Station
  摘要：Differential equations are customarily used to describe dynamic systems. Existing methods for estimating unknown parameters in those systems include parameter cascade, which is a spline-based technique, and pseudo-least-squares, which is a local-polynomial-based two-step method. Parameter cascade is often referred to as a 'one-step method', although it in fact involves at least two stages: one to choose the tuning parameter and another to select model parameters. We propose a class of fast, ea...